Yu Akagi

11 papers receiving 218 citations

Hit Papers

Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review 2024 · 58 citations
580+1Years since publication1020304050

Peers

Yu Akagi
Comparison fields: 5 of 71
  • Clinical Psychology 84
  • Health Informatics 5
  • General Health Professions 62
  • Neurology 33
  • Infectious Diseases 32
Replace Muhammad Hassan with:
Muhammad Hassan Pakistan
Bala Munipalli United States
Dacre Knight United States
Ferran Espuny Pujol United Kingdom
Oliver Ibarrondo Spain
Priya Sharma India
Bahadar S. Srichawla United States
Urs Mücke Germany
Abdelwahab Aleshawi Jordan
Kristen Osinski United States
Yu Akagi relative to Muhammad Hassan Pakistan Muhammad Hassan's profile →
Citations per field
00.5×1.5×2.5×
Muhammad Hassan · 1×
Citations per year

Countries citing papers authored by Yu Akagi

Since Specialization
Citations

This map shows the geographic impact of Yu Akagi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yu Akagi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu Akagi more than expected).

Fields of papers citing papers by Yu Akagi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yu Akagi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yu Akagi. The network helps show where Yu Akagi may publish in the future.

Co-authors

The 25 scholars most cited alongside Yu Akagi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yu Akagi Line = papers co-authored together Yu Akagi links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 202096
2
Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review
Hit paper breakdown →
202458
3 202038
4 202018
5 20253
6 20252
7 20252
8 20232
9
[Migratory pneumonitis similar to bronchiolitis obliterans organizing pneumonia after conservative treatment of breast cancer: a case report].
19992
10 20212
11
Pathomorphological study on portal hypertension a study of schistosomiasis japonica in man 1
19851
12 20250
13 20250
14 20240
15 20250

About Yu Akagi

Yu Akagi is a scholar working on Surgery, Cardiology and Cardiovascular Medicine, Infectious Diseases, Dermatology and Neurology, having authored 15 papers that have together received 224 indexed citations. Recurring topics across this work include Hemodynamic Monitoring and Therapy (3 papers), Cardiac, Anesthesia and Surgical Outcomes (3 papers), Long-Term Effects of COVID-19 (2 papers), COVID-19 diagnosis using AI (2 papers), Autoimmune and Inflammatory Disorders Research (2 papers), Health Systems, Economic Evaluations, Quality of Life (2 papers), Blood Pressure and Hypertension Studies (2 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (1 paper). The work is most often cited by research in Clinical Psychology (84 citations), Health Informatics (5 citations), General Health Professions (62 citations), Neurology (33 citations) and Infectious Diseases (32 citations). Yu Akagi has collaborated with scholars based in Japan and United Kingdom. Frequent co-authors include Naoyuki Kuse, Minoru Inomata, Takehiro Izumo, Kohei Takada, Nobuyasu Awano, Mari Tone, Momoko Mawatari, Akihiro Ueda, Yutaka Muto and Kazushi Fujimoto. Their work appears in journals such as Scientific Reports, Circulation Journal, Journal of Medical Systems, Frontiers in Cardiovascular Medicine and Journal of clinical lipidology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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